An exact, unified distributional characterization of statistics used to test linear hypotheses in simple regression models
AbstractThe Wald, likelihood ratio and Lagrange multiplier test statistics are commonly used to test linear restrictions in regression models. It is shown that for testing these restrictions in the classical regression model, the exact densities of these test statistics are special cases of the generalized beta distribution introduced by McDonald (1984); McDonald and Xu (1995a). This unified derivation provides a method by which one can derive small sample critical values for each test. These results may be indicative of the behavior of such test statistics in more general settings, and are useful in visualizing how each statistic changes with different parameter values in the simple regression model. For example, the results suggest that Wald tests may severely underreject the null hypothesis when the sample size is small or a large number of restrictions are tested.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 22841.
Date of creation: 20 May 2010
Date of revision:
Test of linear restrictions; Generalized beta distribution; Small-sample probability distribution; Regression model;
Find related papers by JEL classification:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
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